Intelligence-based medicine : artificial intelligence and human cognition in clinical medicine and healthcare / Anthony C. Chang.

Format:

Book

Author/Creator:

Chang, Anthony C., author.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Medical informatics.

Artificial Intelligence.

Medical Informatics.

Medical Subjects:

Artificial Intelligence.

Medical Informatics.

Physical Description:

xxvi, 521 pages : illustrations (chiefly color) ; 24 cm

Place of Publication:

London, United Kingdom ; San Diego, CA, United States : Academic Press is an imprint of Elsevier, [2020]

Contents:

Machine generated contents note: I. Introduction to artificial intelligence

1. Basic Concepts of Artificial Intelligence

Definitions

Types of Artificial Intelligence

Artificial Intelligence and Data Science

The Human

-Machine Intelligence Continuum

The Analytics Continuum

Artificial Intelligence and the Neurosciences

The Doctor's Brain and Machine Intelligence

References

2. History of Artificial Intelligence

Key People and Events

Alan Turing and the Turing Machine

The Dartmouth Conference

Rosenblatt's Perceptron

Key Epochs and Movements

Good Old-fashioned Artificial Intelligence

Computational Intelligence

The Artificial Intelligence Winters

3. History of Artificial Intelligence in Medicine

Rule-based Expert Systems

Other Artificial Intelligence Methodologies

Failure of Adoption

Ten Common Misconceptions of Artificial Intelligence in Medicine

Key Concepts

II. Data science and artificial intelli' gence in the current era

4. Health-care Data and Databases

Health-care Data

Big Data

Health-care Data Conundrum

Health-care Data Management

Data Processing and Storage

Electronic Medical Record Adoption and Interoperability

Health-care Databases

Database-management Systems

Relational Database

Object-oriented Database

Graph Database

The Data-to-intelligence Continuum and Artificial Intelligence

5. Machine and Deep Learning

Introduction to Machine Learning

Data Mining and Knowledge Discovery

History and Current State of Machine Learning

Machine Learning versus Conventional Programing

Machine Learning Workflow

Data Science in Biomedicine

Programing Languages in Biomedical Data Science

Classical Machine Learning

Supervised Learning

Unsupervised Learning

Ensemble Learning

Reinforcement Learning

Neural Networks and Deep Learning

Perceptron and Multilayer Perceptrons

Deep Learning

Autoencoder Neural Network

Assessment of Model Performance

Assessment Methods

Evaluation of Regression Models

Evaluation of Classification Models

Fundamental Issues in Machine and Deep Learning

Interpretability and Explainability

Bias and Variance Trade-off

Fitting

Curse of Dimensionality

Correlation versus Causation

Machine versus Deep Learning

6. Other Key Concepts in Artificial Intelligence

Cognitive Computing

Natural Language Processing

Robotics

Autonomous Systems

Robotic Process Automation

Other Key Technologies Related to Artificial Intelligence

Augmented and virtual reality

Blockchain

Cloud

Cybersecurity

Internet of Things

Key Issues Related to Artificial Intelligence

Bias

Ethics

Safety

Legal

Ten Questions to Assess your Data Science and Artificial Intelligence Knowledge

Ten Steps to Become More Knowledgeable in Artificial Intelligence in Medicine

III. The current era of artificial intelligence in medicine

7. Clinician Cognition and Artificial Intelligence in Medicine

The Rationale for Intelligence-based Medicine

Adoption of Artificial Intelligence in Medicine: The Challenges Ahead

Clinician Cognition and Artificial Intelligence in Medicine

Current Artificial Intelligence in Medicine Applications

8. Artificial Intelligence in Subspecialties

The Present State of Artificial Intelligence in Subspecialties

The subspecialties and artificial intelligence strategy and applications

Artificial Intelligence Applications for all Subspecialties

Anesthesiology

Cardiology (Adult and Pediatric) and Cardiac Surgery

Critical Care Medicine

Dermatology

Emergency Medicine

Endocrinology

Gastroenterology

Global and Public Health/Epidemiology

Hematology

Infectious Disease

Internal and Family Medicine/Primary Care

Nephrology

Neurosciences (Neurology/Neurosurgery and Psychiatry/Psychology)

Obstetrics/Gynecology

Oncology

Ophthalmology

Pathology

Pediatrics

Pulmonology

Radiology

Rheumatology

Surgery

Dentistry

Digital Health

Genomic Medicine and Precision/Personalized Medicine

Physical Medicine and Rehabilitation

Regenerative Medicine

Veterinary Medicine

Medical Education and Training

Nursing

Healthcare Administration

9. Implementation of Artificial Intelligence in Medicine

Assessment of Artificial Intelligence Readiness in Health-care Organizations

Ten Elements for Successful Implementation of Artificial Intelligence in Medicine

Ten Obstacles to Overcome for Implementation of Artificial Intelligence in Medicine

IV. The future of artificial intelligence and application in medicine

10. Key Concepts of the Future of Artificial Intelligence

5G

Augmented and Virtual Reality

Blockchain and Cybersecurity

Brain

-Computer Interface

Capsule Network

Cloud Artificial Intelligence

Edge Computing

Embedded Artificial Intelligence (or Internet of Everything)

Fuzzy Cognitive Maps

Generative Query Network

Hypergraph Database

Low-shot Learning

Neuromorphic Computing

Quantum Computing

Recursive Cortical Network

Spiking Neural Network (SNN)

Swarm Intelligence

Temporal Convolutional Nets

Transfer Learning

Data and Databases

11. The Future of Artificial Intelligence in Medicine

References.

Notes:

Includes bibliographical references and index.

Other Format:

Electronic version: Chang, Anthony C. Intelligence-based medicine.

ISBN:

9780128164624

012816462X

9780128233375

0128233370

OCLC:

1139179519